Load Balancing of Sensory Data for IoT Deep Learning Applications with Edge/Fog Computing (VSI-lbdl)

Overview:

In the last decade, evolution of Internet of Things (IoT) has controlled various application domains such as emergency management, industrial applications, health care systems, real-time systems, and has been foreseen to flourish in future. The populace of IoT-based devices has been extended to 30 billion and expected to surpass 500 billion by 2030. This fact has led to various challenges, like how to extract and manage huge amounts of data that are constantly generated by IoT devices. This gives rise to solutions for IoT based on edge/fog computing that can move the data processing towards the edge of network as cloud-based deployments are not able to meet the increasing demands of clients. Nowadays, the Deep learning (DL) approach is used to extract information from IoT devices that are deployed in edge/fog computing environment due to its multi-layer structuring. DL in IoT is performed as layered processing in a distributed manner between edge and fog by extracting new features automatically for different problems. For the extraction of information from huge volume of real time data of IoT devices, data processing needs to be done close to the end devices where the data is produced (the edge). Since the prevailing edge nodes have inadequate processing capability, it is necessary to design and develop efficient load balancing strategies to optimize the performance of IoT devices for deep learning applications with edge/fog computing.

The aim of this special section is to deal with various challenges such as hardware design, theoretical modeling, system architecture and analysis of deep learning applications in IoT using edge/fog computing. The focus is therefore on high-quality original papers aiming at demonstrating effective and efficient DL approaches for edge/fog, which at the same time considers data, device and infrastructure perspectives and related issues. This special section will cover recent technical advances with respect to IoT edge/fog computing, including strategies and protocols, architectures, emerging models, test-beds, applications, systems and field deployments. Nevertheless, numerous challenges exist for synchronization of the edge devices with other edge devices and fog under vibrant network circumstances and diverse processing competencies to deal with privacy issue and application-level performance.

Topics:

The topics of interest include:

• Protocols, models and architectures
• Resource allocation and management
• Data processing, distribution, management, and storage
• Computation offloading from IoT deep learning applications with edge/fog computing
• Fog-Cloud computing for IoT systems
• Security, privacy and trust
• AI and machine learning
• Performance evaluation
• Services and applications
• Edge-based platforms for IoT deep learning applications
• Novel edge/fog based deep learning models for IoT
• Simulation of edge IoT mining
• Methodologies for driving deep learning-based IoT edge/fog systems development
• Real applications and systems of deep learning at the edge

Submission Guidelines:

New papers, or extended versions of papers presented at related conferences, are welcome. Submissions must not be currently under review for publication elsewhere. Conference papers may be submitted only if they are substantially extended (more than 50%), and must be referenced. All submitted papers will be peer-reviewed using the normal standards of CAEE, and accepted based on quality, originality, novelty, and relevance to the theme of the special section. By submitting a paper to this issue, authors agree to review one paper (if asked) within the time frame of the special section.

Before submission, authors should carefully read the Guide for Authors available at

https://www.elsevier.com/journals/computers-and-electrical-engineering/0045-7906/guide-for-authors

Authors should submit their papers through the journal’s web submission tool at https://www.editorialmanager.com/compeleceng/default.aspx by selecting “VSI- lbdl” under the “Issues” tab.

For additional questions, contact the Main Guest Editor.

Schedule:

Submission of manuscript: September 01, 2020

First notification: November 25, 2020

Submission of revised manuscript: December 30, 2020

Notification of the re-review: January 30, 2021

Final notification: February 28, 2021

Final paper due: March 31, 2021

Publication: June. 2021

Guest Editors:

Bhisham Sharma, Ph.D. (Managing Guest Editor)

Chitkara University School of Engineering and Technology,

Chitkara University, Himachal Pradesh, India

Email: bhisham.pec@gmail.com

Deepika Koundal, Ph.D.

Department of Virtualization, School of Computer Science,

University of Petroleum and Energy Studies, Dehradun, India

Email: dkoundal@ddn.upes.ac.in

Raman Singh, Ph.D.

Post-Doctoral Fellow, School of Computer Science and Statistics, Trinity College Dublin, Ireland

Email: rasingh@tcd.ie

Guest Editors’ bios:

Bhisham Sharma received a Ph.D. in Computer Science & Engineering from the PEC University of Technology (Formerly Punjab Engineering College), Chandigarh, India. He is currently working as an Associate Professor in the Department of Computer Science and Engineering, Chitkara University, Himachal Pradesh, India. He is having 10 years of teaching and research experience at various reputed Universities of India. He has received the Excellence Award for publishing research papers with highest H-index given by Chitkara University in 2020. He is also a reviewer for more than 10 journals such as Future Generation Computing Systems, IEEE Access, Computer Networks, Frontier of Computer Science, International Journal of Communication Systems, IEEE Transactions on Reliability and so on. His research interests include Mobile Computing, Wireless Communication, Wireless Sensor Networks, Wireless Mesh Networks, Next Generation Networking, Network Security, Internet of Things, and Edge/Fog Computing in which he has published over 35 research papers in reputed SCI and Scopus indexed journals, international conferences and book chapters.

Deepika Koundal is currently associated with University of Petroleum and Energy Studies, Dehradun. She is having 12 years of teaching and research experience at various reputed Universities of India. She received her B.Tech. Degree in Computer Science and Engineering from Kurukshetra University, Kurukshetra, Haryana, India and subsequently her M.E. and Ph.D. Degrees in Computer Science & Engineering from UIET, Panjab University, Chandigarh, India. Her Ph.D. thesis is focused on Automated delineation of thyroid nodules in Ultrasound Images. She also received the recognition and honorary membership from Neutrosophic Science Association from University of Mexico for her outstanding publication in the Applied Soft Computing Journal. She has published more than 30 research articles in reputed SCI and Scopus indexed journals, conferences and two books.

Raman Singh is working as Post-Doctoral Fellow at School of Computer Science and Statistics, Trinity College Dublin, The University of Dublin, Ireland. He also holds position of Assistant Professor in Computer Science and Engineering Department of Thapar Institute of Engineering and Technology, Patiala (India). He is working in research area next generation networking to replace IP based architecture with Decentralized Web. He has completed Ph.D. (CSE) from University Institute of Engineering and Technology, Panjab University Chandigarh in February 2016. He has completed Master of Engineering (IT) from UIET, Panjab University Chandigarh in May 2010. He has published 14 research papers in international journals and conferences. He has won Best Publication of the Year – 2016 award from UIET Panjab University. He has 10 years of teaching and research experience. He has served Information Technology industry for 2 years as a technology solution consultant. He is a Microsoft Certified Technology Specialist (MCTS) and Microsoft TechNet Certified Technology Expert. His area of interest includes Next Generation Networking, Block chain Technologies, Intrusion Detection, Network Security, Cyber Security, Autonomous Driving Deep Learning, and IoT.